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arXiv · 2211.13014

Sarcasm Detection Framework Using Context, Emotion and Sentiment Features

Oxana Vitman·Yevhen Kostiuk·Grigori Sidorov·Alexander Gelbukh
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Abstract

Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, that incorporates different features to capture the incongruity intrinsic to sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.

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Oxana Vitman, Yevhen Kostiuk, Grigori Sidorov, Alexander Gelbukh. 2022-11-23. Sarcasm Detection Framework Using Context, Emotion and Sentiment Features. https://arxiv.org/abs/2211.13014

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